stable-diffusion.cpp

Inference of Stable Diffusion in pure C/C++

Features

• Plain C/C++ implementation based on ggml, working in the same way as llama.cpp
• 16-bit, 32-bit float support
• 4-bit, 5-bit and 8-bit integer quantization support
• Accelerated memory-efficient CPU inference
  • Only requires ~2.3GB when using txt2img with fp16 precision to generate a 512x512 image
• AVX, AVX2 and AVX512 support for x86 architectures
• SD1.x and SD2.x support
• Original txt2img and img2img mode
• Negative prompt
• stable-diffusion-webui style tokenizer (not all the features, only token weighting for now)
• Sampling method
  • Euler A
  • Euler
  • Heun
  • DPM++ 2M
  • DPM++ 2M v2
• Cross-platform reproducibility (--rng cuda, consistent with the stable-diffusion-webui GPU RNG)
• Supported platforms
  • Linux
  • Mac OS
  • Windows
  • Android (via Termux)

TODO

• More sampling methods
• GPU support
• Make inference faster
  • The current implementation of ggml_conv_2d is slow and has high memory usage
• Continuing to reduce memory usage (quantizing the weights of ggml_conv_2d)
• LoRA support
• k-quants support

Usage

Get the Code

git clone --recursive https://github.com/leejet/stable-diffusion.cpp
cd stable-diffusion.cpp

• If you have already cloned the repository, you can use the following command to update the repository to the latest code.

cd stable-diffusion.cpp
git pull origin master
git submodule init
git submodule update

Convert weights

• download original weights(.ckpt or .safetensors). For example

  • Stable Diffusion v1.4 from https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
  • Stable Diffusion v1.5 from https://huggingface.co/runwayml/stable-diffusion-v1-5
  • Stable Diffuison v2.1 from https://huggingface.co/stabilityai/stable-diffusion-2-1

  curl -L -O https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
  # curl -L -O https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
  # curl -L -o https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.safetensors
  

• convert weights to ggml model format

  cd models
  pip install -r requirements.txt
  python convert.py [path to weights] --out_type [output precision]
  # For example, python convert.py sd-v1-4.ckpt --out_type f16
  

Quantization

You can specify the output model format using the --out_type parameter

• f16 for 16-bit floating-point
• f32 for 32-bit floating-point
• q8_0 for 8-bit integer quantization
• q5_0 or q5_1 for 5-bit integer quantization
• q4_0 or q4_1 for 4-bit integer quantization

Build

Build from scratch

mkdir build
cd build
cmake ..
cmake --build . --config Release

Using OpenBLAS

cmake .. -DGGML_OPENBLAS=ON
cmake --build . --config Release

Run

usage: ./bin/sd [arguments]

arguments:
  -h, --help                         show this help message and exit
  -M, --mode [txt2img or img2img]    generation mode (default: txt2img)
  -t, --threads N                    number of threads to use during computation (default: -1).
                                     If threads <= 0, then threads will be set to the number of CPU physical cores
  -m, --model [MODEL]                path to model
  -i, --init-img [IMAGE]             path to the input image, required by img2img
  -o, --output OUTPUT                path to write result image to (default: .\output.png)
  -p, --prompt [PROMPT]              the prompt to render
  -n, --negative-prompt PROMPT       the negative prompt (default: "")
  --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
  --strength STRENGTH                strength for noising/unnoising (default: 0.75)
                                     1.0 corresponds to full destruction of information in init image
  -H, --height H                     image height, in pixel space (default: 512)
  -W, --width W                      image width, in pixel space (default: 512)
  --sampling-method {euler, euler_a, heun, dpm++2m, dpm++2mv2}
                                     sampling method (default: "euler_a")
  --steps  STEPS                     number of sample steps (default: 20)
  --rng {std_default, cuda}          RNG (default: cuda)
  -s SEED, --seed SEED               RNG seed (default: 42, use random seed for < 0)
  -v, --verbose                      print extra info

txt2img example

./bin/sd -m ../models/sd-v1-4-ggml-model-f16.bin -p "a lovely cat"

Using formats of different precisions will yield results of varying quality.

f32	f16	q8_0	q5_0	q5_1	q4_0	q4_1

img2img example

• ./output.png is the image generated from the above txt2img pipeline

./bin/sd --mode img2img -m ../models/sd-v1-4-ggml-model-f16.bin -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4

Docker

Building using Docker

docker build -t sd .

Run

docker run -v /path/to/models:/models -v /path/to/output/:/output sd [args...]
# For example
# docker run -v ./models:/models -v ./build:/output sd -m /models/sd-v1-4-ggml-model-f16.bin -p "a lovely cat" -v -o /output/output.png

Memory/Disk Requirements

precision	f32	f16	q8_0	q5_0	q5_1	q4_0	q4_1
Disk	2.7G	2.0G	1.7G	1.6G	1.6G	1.5G	1.5G
Memory(txt2img - 512 x 512)	~2.8G	~2.3G	~2.1G	~2.0G	~2.0G	~2.0G	~2.0G

References

• ggml
• stable-diffusion
• stable-diffusion-stability-ai
• stable-diffusion-webui
• k-diffusion